def _plot_default(self):
# Set up the spectrum plot
spectrum_plot = Plot(self.spectrum_data)
spectrum_plot.plot(("frequency", "amplitude"),
name="Spectrum",
color="red")
spectrum_plot.padding = 50
spectrum_plot.title = "Spectrum"
spec_range = list(
spectrum_plot.plots.values())[0][0].value_mapper.range # noqa
spec_range.low = 0.0
spec_range.high = 5.0
spectrum_plot.index_axis.title = 'Frequency (Hz)'
spectrum_plot.value_axis.title = 'Amplitude'
# Time series plot
time_plot = Plot(self.time_data)
time_plot.plot(("time", "amplitude"), name="Time", color="blue")
time_plot.padding = 50
time_plot.title = "Time"
time_plot.index_axis.title = 'Time (seconds)'
time_plot.value_axis.title = 'Amplitude'
time_range = list(time_plot.plots.values())[0][0].value_mapper.range
time_range.low = -0.2
time_range.high = 0.2
# Spectrogram plot
spectrogram_plot = Plot(self.spectrogram_plotdata)
max_time = SPECTROGRAM_LENGTH * NUM_SAMPLES / SAMPLING_RATE
max_freq = SAMPLING_RATE / 2
spectrogram_plot.img_plot(
'imagedata',
name='Spectrogram',
xbounds=(0, max_time),
ybounds=(0, max_freq),
colormap=hot,
)
range_obj = spectrogram_plot.plots['Spectrogram'][0].value_mapper.range
range_obj.high = 5
range_obj.low = 0.0
spectrogram_plot.title = 'Spectrogram'
container = HPlotContainer()
container.add(spectrum_plot)
container.add(time_plot)
container.add(spectrogram_plot)
return container
def _create_plot_component():
# Create some data
index, sorted_vals = _create_data(200)
# Create a plot data obect and give it this data
pd = ArrayPlotData(index = index,
min = sorted_vals[0],
bar_min = sorted_vals[1],
average = sorted_vals[2],
bar_max = sorted_vals[3],
max = sorted_vals[4])
# Create the plot
plot = Plot(pd)
plot.candle_plot(("index", "min", "bar_min", "average", "bar_max", "max"),
color = "lightgray",
bar_line_color = "black",
stem_color = "blue",
center_color = "red",
center_width = 2)
# Tweak some of the plot properties
plot.title = "Candlestick Plot"
plot.line_width = 0.5
plot.padding = 50
# Attach some tools to the plot
plot.tools.append(PanTool(plot, constrain_key="shift"))
zoom = ZoomTool(component=plot, tool_mode="box", always_on=False)
plot.overlays.append(zoom)
return plot
def _create_plot_component():
# Create a GridContainer to hold all of our plots
container = GridContainer(padding=20, fill_padding=True,
bgcolor="lightgray", use_backbuffer=True,
shape=(3,3), spacing=(12,12))
# Create the initial series of data
x = linspace(-5, 15.0, 100)
pd = ArrayPlotData(index = x)
# Plot some bessel functions and add the plots to our container
for i in range(9):
pd.set_data("y" + str(i), jn(i,x))
plot = Plot(pd)
plot.plot(("index", "y" + str(i)),
color=tuple(COLOR_PALETTE[i]), line_width=2.0,
bgcolor = "white", border_visible=True)
# Tweak some of the plot properties
plot.border_width = 1
plot.padding = 10
# Set each plot's aspect ratio based on its position in the
# 3x3 grid of plots.
n,m = divmod(i, 3)
plot.aspect_ratio = float(n+1) / (m+1)
# Attach some tools to the plot
plot.tools.append(PanTool(plot))
zoom = ZoomTool(plot, tool_mode="box", always_on=False)
plot.overlays.append(zoom)
# Add to the grid container
container.add(plot)
return container
def _plot_default(self):
self.data = self._data_default()
plot = Plot(self.data)
for ii, s_name in enumerate(self._samples):
color = COLORS[ii % len(self._samples)]
plot.plot(
("bins", s_name),
name=s_name,
type="filled_line",
edge_color=color,
face_color=color,
alpha=0.5,
bgcolor="white",
render_style="hold",
) # render_style determines whether interpolate
plot.index = plot._get_or_create_datasource("bins") # set index name manually so range selection works
plot.index_scale = "log"
plot.title = "Fermi Plot"
plot.padding = 50
plot.legend.visible = True
plot.tools.append(PanTool(plot))
plot.active_tool = RangeSelection(plot)
plot.overlays.append(RangeSelectionOverlay(component=plot))
zoom = ZoomTool(component=plot, tool_mode="box", always_on=False)
plot.overlays.append(zoom)
return plot
def _create_plot_component():
# Create a scalar field to colormap
xs = linspace(0, 10, 600)
ys = linspace(0, 5, 600)
x, y = meshgrid(xs,ys)
z = exp(-(x**2+y**2)/100)
# Create a plot data obect and give it this data
pd = ArrayPlotData()
pd.set_data("imagedata", z)
# Create the plot
plot = Plot(pd)
img_plot = plot.img_plot("imagedata",
xbounds=(0, 10),
ybounds=(0, 5),
colormap=jet)[0]
# Tweak some of the plot properties
plot.title = "My First Image Plot"
plot.padding = 50
# Attach some tools to the plot
plot.tools.append(PanTool(plot))
zoom = ZoomTool(component=img_plot, tool_mode="box", always_on=False)
img_plot.overlays.append(zoom)
return plot
def _create_plot_component():
# Create some data
numpts = 5000
x = sort(random(numpts))
y = random(numpts)
# Create a plot data object and give it this data
pd = ArrayPlotData()
pd.set_data("index", x)
pd.set_data("value", y)
# Create the plot
plot = Plot(pd)
# Tweak some of the plot properties
plot.title = "Scatter Plot"
plot.line_width = 0.5
plot.padding = 50
# Attach some tools to the plot
plot.tools.append(PanTool(plot, constrain_key="shift"))
zoom = ZoomTool(component=plot, tool_mode="box", always_on=False)
plot.overlays.append(zoom)
return plot
def _pot_plot_default(self):
plot = Plot(self.plot_data)
plot.plot(('potentiometer',))
plot.padding = (0, 0, 0, 0)
plot.value_mapper.range.low_setting = 0
plot.value_mapper.range.high_setting = 1
return plot
def _acc_x_plot_default(self):
plot = Plot(self.plot_data)
plot.plot(('acc_x',))
plot.padding = (0, 0, 0, 0)
plot.value_mapper.range.low_setting = 0
plot.value_mapper.range.high_setting = 1
return plot
def _create_plot_component(): # Create a scalar field to colormap
xbounds = (-2 * pi, 2 * pi, 600)
ybounds = (-1.5 * pi, 1.5 * pi, 300)
xs = linspace(*xbounds)
ys = linspace(*ybounds)
x, y = meshgrid(xs, ys)
z = sin(x) * y
# Create a plot data obect and give it this data
pd = ArrayPlotData()
pd.set_data("imagedata", z)
# Create the plot
plot = Plot(pd)
img_plot = plot.img_plot("imagedata",
xbounds=xbounds[:2],
ybounds=ybounds[:2],
colormap=viridis)[0]
# Tweak some of the plot properties
plot.title = "Image Plot with Lasso"
plot.padding = 50
lasso_selection = LassoSelection(component=img_plot)
lasso_selection.on_trait_change(lasso_updated, "disjoint_selections")
lasso_overlay = LassoOverlay(lasso_selection=lasso_selection,
component=img_plot)
img_plot.tools.append(lasso_selection)
img_plot.overlays.append(lasso_overlay)
return plot
def _plot_default(self):
plot = Plot(self.plot_data)
plot.x_axis = None
plot.y_axis = None
plot.x_grid = None
plot.y_grid = None
plot.padding = 0
plot.plot(('x', 'image_histogram'), render_style='connectedhold')
plot.plot(('x', 'mapped_histogram'), type='filled_line', fill_color='yellow',
render_style='connectedhold', name='mapped_histogram')
intercept_tool = AttributeDragTool(component=plot, model=self.unit_map,
x_attr='intercept')
slope_tool = AttributeDragTool(component=plot, model=self.unit_map,
x_attr='slope', modifier_keys=set(['shift']))
gamma_tool = AttributeDragTool(component=plot, model=self.unit_map,
x_attr='gamma', modifier_keys=set(['control']))
plot.tools += [intercept_tool, slope_tool, gamma_tool]
intercept_overlay = SimpleInspectorOverlay(component=plot, align='ul',
inspector=intercept_tool,
field_formatters=[[basic_formatter('Intercept', 2)]]
)
slope_overlay = SimpleInspectorOverlay(component=plot, align='ul',
inspector=slope_tool,
field_formatters=[[basic_formatter('Slope', 2)]]
)
gamma_overlay = SimpleInspectorOverlay(component=plot, align='ul',
inspector=gamma_tool,
field_formatters=[[basic_formatter('Gamma', 2)]]
)
plot.overlays += [intercept_overlay, slope_overlay, gamma_overlay]
return plot
def _create_plot_component():
# Create some x-y data series to plot
x = linspace(1.0, 8.0, 200)
pd = ArrayPlotData(index = x)
pd.set_data("y0", sqrt(x))
pd.set_data("y1", x)
pd.set_data("y2", x**2)
pd.set_data("y3", exp(x))
pd.set_data("y4", gamma(x))
pd.set_data("y5", x**x)
# Create some line plots of some of the data
plot = Plot(pd)
plot.plot(("index", "y0"), line_width=2, name="sqrt(x)", color="purple")
plot.plot(("index", "y1"), line_width=2, name="x", color="blue")
plot.plot(("index", "y2"), line_width=2, name="x**2", color="green")
plot.plot(("index", "y3"), line_width=2, name="exp(x)", color="gold")
plot.plot(("index", "y4"), line_width=2, name="gamma(x)",color="orange")
plot.plot(("index", "y5"), line_width=2, name="x**x", color="red")
# Set the value axis to display on a log scale
plot.value_scale = "log"
# Tweak some of the plot properties
plot.title = "Log Plot"
plot.padding = 50
plot.legend.visible = True
# Attach some tools to the plot
plot.tools.append(PanTool(plot))
zoom = ZoomTool(component=plot, tool_mode="box", always_on=False)
plot.overlays.append(zoom)
return plot
def _create_plot_component():
# Create some data
index, vals = _create_data(20)
# Create a plot data object and give it this data
pd = ArrayPlotData(index = index,
values = vals)
# Create the plot
plot = Plot(pd)
plot.stacked_bar_plot(("index", "values"),
color = ["red", "yellow", "green", "blue"],
outline_color = "lightgray",)
# Tweak some of the plot properties
plot.title = "Stacked Bar Plot"
plot.line_width = 0.5
plot.padding = 50
# Attach some tools to the plot
plot.tools.append(PanTool(plot, constrain_key="shift"))
zoom = ZoomTool(component=plot, tool_mode="box", always_on=False)
plot.overlays.append(zoom)
return plot
def _create_plot_component():
# Create some data
numpts = 5000
x = sort(random(numpts))
y = random(numpts)
# Create a plot data obect and give it this data
pd = ArrayPlotData()
pd.set_data("index", x)
pd.set_data("value", y)
# Create the plot
plot = Plot(pd)
plot.plot(("index", "value"),
type="scatter",
marker="circle",
index_sort="ascending",
color="orange",
marker_size=3,
bgcolor="white")
# Tweak some of the plot properties
plot.title = "Scatter Plot"
plot.line_width = 0.5
plot.padding = 50
# Attach some tools to the plot
plot.tools.append(PanTool(plot, constrain_key="shift"))
zoom = ZoomTool(component=plot, tool_mode="box", always_on=False)
plot.overlays.append(zoom)
return plot
def _create_plot_component(obj):
# Spectrogram plot
obj.data = fr.getDataSets(".chi")
frequencies = obj.data[0][0]
index = ArrayDataSource(data=frequencies)
values = [obj.data[i][1] for i in xrange(len(obj.data))]
print len(obj.data[1][1])
print len(obj.data)
p = WaterfallRenderer(
index=index,
values=values,
index_mapper=LinearMapper(range=DataRange1D(low=0, high=SPECTROGRAM_LENGTH)),
value_mapper=LinearMapper(range=DataRange1D(low=0, high=SPECTROGRAM_LENGTH)),
x2_mapper=LinearMapper(low_pos=0, high_pos=100, range=DataRange1D(low=10.0, high=101.0)),
y2_mapper=LinearMapper(low_pos=0, high_pos=100, range=DataRange1D(low=0, high=600000)),
)
spectrogram_plot = p
obj.spectrogram_plot = p
dummy = Plot()
dummy.padding = 50
dummy.index_axis.mapper.range = p.index_mapper.range
dummy.index_axis.title = "Frequency (hz)"
dummy.add(p)
c2 = VPlotContainer()
c2.add(dummy)
return c2
def _create_plot_component():
# Create some data
numpts = 1000
x = sort(random(numpts))
y = random(numpts)
color = randint(0, 7, numpts)
# Create a plot data obect and give it this data
pd = ArrayPlotData()
pd.set_data("index", x)
pd.set_data("value", y)
pd.set_data("color", color)
# Create the plot
plot = Plot(pd)
plot.plot(("index", "value", "color"),
type="cmap_scatter",
name="my_plot",
color_mapper=accent,
marker="square",
fill_alpha=0.5,
marker_size=6,
outline_color="black",
border_visible=True,
bgcolor="white")
# Tweak some of the plot properties
plot.title = "Colormapped Scatter Plot with Range-selectable Data Points"
plot.padding = 50
plot.x_grid.visible = False
plot.y_grid.visible = False
plot.x_axis.font = "modern 16"
plot.y_axis.font = "modern 16"
# Right now, some of the tools are a little invasive, and we need the
# actual ColomappedScatterPlot object to give to them
cmap_renderer = plot.plots["my_plot"][0]
# Attach some tools to the plot
plot.tools.append(PanTool(plot, constrain_key="shift"))
zoom = ZoomTool(component=plot, tool_mode="box", always_on=False)
plot.overlays.append(zoom)
selection = ColormappedSelectionOverlay(cmap_renderer,
fade_alpha=0.35,
selection_type="mask")
cmap_renderer.overlays.append(selection)
# Create the colorbar, handing in the appropriate range and colormap
colorbar = create_colorbar(plot.color_mapper)
colorbar.plot = cmap_renderer
colorbar.padding_top = plot.padding_top
colorbar.padding_bottom = plot.padding_bottom
# Create a container to position the plot and the colorbar side-by-side
container = HPlotContainer(use_backbuffer=True)
container.add(plot)
container.add(colorbar)
container.bgcolor = "lightgray"
return container
def _create_plot_component():
# Create some data
numpts = 500
x1 = random(numpts)
y1 = random(numpts)
x2 = x1 + standard_normal(numpts) * 0.05
y2 = y1 + standard_normal(numpts) * 0.05
# Create a plot data object and give it this data
pd = ArrayPlotData()
pd.set_data("index", column_stack([x1, x2]).reshape(-1))
pd.set_data("value", column_stack([y1, y2]).reshape(-1))
# Create the plot
plot = Plot(pd)
plot.plot(("index", "value"),
type="segment",
color="forestgreen",
line_width=2,
line_style='dash',
alpha=0.7,
bgcolor="white")
# Tweak some of the plot properties
plot.title = "Segment Plot"
plot.line_width = 0.5
plot.padding = 50
# Attach some tools to the plot
plot.tools.append(PanTool(plot, constrain_key="shift"))
zoom = ZoomTool(component=plot, tool_mode="box", always_on=False)
plot.overlays.append(zoom)
return plot
def _create_plot_component():
# Create a GridContainer to hold all of our plots: 2 rows, 3 columns:
container = GridContainer(padding=40,
fill_padding=True,
bgcolor="lightgray",
use_backbuffer=True,
shape=(2, 3),
spacing=(20, 20))
# Create the initial series of data
x = linspace(-5, 15.0, 100)
pd = ArrayPlotData(index=x)
# Plot some bessel functions and add the plots to our container
for i in range(6):
pd.set_data("y" + str(i), jn(i, x))
plot = Plot(pd)
plot.plot(("index", "y" + str(i)),
color=tuple(COLOR_PALETTE[i]),
line_width=2.0,
bgcolor="white",
border_visible=True)
# Tweak some of the plot properties
plot.border_width = 1
plot.padding = 0
plot.padding_top = 30
# Attach some tools to the plot
plot.tools.append(PanTool(plot))
zoom = ZoomTool(plot, tool_mode="box", always_on=False)
plot.overlays.append(zoom)
# Add to the grid container (
container.add(plot)
# Set the upper-left plot to only be resizable vertically, and to have a
# fixed horizontal width. This also constrains the width of the first column.
ul_plot = container.components[0]
ul_plot.set(resizable="v", width=200)
ul_plot.overlays.append(
PlotLabel("Not horizontally resizable", component=ul_plot))
# Set the bottom center plot to have a fixed width and height.
# This also constrains the height of the bottom row and the width of
# the middle column.
cplot = container.components[4]
cplot.set(resizable="", bounds=[400, 400])
cplot.overlays.append(PlotLabel("Not resizable", component=cplot))
container.padding_top = 50
container.overlays.append(
PlotLabel(
'Resize the window - some plots resize, others cannot '
'(see source code)',
component=container,
font="swiss 16",
overlay_position="top"))
return container
def _create_plot_component():
# Create some x-y data series (with NaNs) to plot
x = linspace(-5.0, 15.0, 500)
x[75:125] = nan
x[200:250] = nan
x[300:330] = nan
pd = ArrayPlotData(index = x)
pd.set_data("value1", jn(0, x))
pd.set_data("value2", jn(1, x))
# Create some line and scatter plots of the data
plot = Plot(pd)
plot.plot(("index", "value1"), name="j_0(x)", color="red", width=2.0)
plot.plot(("index", "value2"), type="scatter", marker_size=1,
name="j_1(x)", color="green")
# Tweak some of the plot properties
plot.title = "Plots with NaNs"
plot.padding = 50
plot.legend.visible = True
# Attach some tools to the plot
plot.tools.append(PanTool(plot))
zoom = ZoomTool(component=plot, tool_mode="box", always_on=False)
plot.overlays.append(zoom)
return plot
def _create_plot_component():
pd = ArrayPlotData(x=random(100), y=random(100))
# Create some line plots of some of the data
plot = Plot(pd)
# Create a scatter plot and get a reference to it (separate from the
# Plot object) because we'll need it for the regression tool below.
scatterplot = plot.plot(("x", "y"), color="blue", type="scatter")[0]
# Tweak some of the plot properties
plot.padding = 50
# Attach some tools to the plot
plot.tools.append(PanTool(plot, drag_button="right"))
plot.overlays.append(ZoomTool(plot))
# Add the regression tool and overlay. These need to be added
# directly to the scatterplot instance (and not the Plot instance).
regression = RegressionLasso(scatterplot,
selection_datasource=scatterplot.index)
scatterplot.tools.append(regression)
scatterplot.overlays.append(
RegressionOverlay(scatterplot, lasso_selection=regression))
return plot
def _create_plot_component():
# Create some x-y data series to plot
x = linspace(1.0, 8.0, 200)
pd = ArrayPlotData(index=x)
pd.set_data("y0", sqrt(x))
pd.set_data("y1", x)
pd.set_data("y2", x**2)
pd.set_data("y3", exp(x))
pd.set_data("y4", gamma(x))
pd.set_data("y5", x**x)
# Create some line plots of some of the data
plot = Plot(pd)
plot.plot(("index", "y0"), line_width=2, name="sqrt(x)", color="purple")
plot.plot(("index", "y1"), line_width=2, name="x", color="blue")
plot.plot(("index", "y2"), line_width=2, name="x**2", color="green")
plot.plot(("index", "y3"), line_width=2, name="exp(x)", color="gold")
plot.plot(("index", "y4"), line_width=2, name="gamma(x)", color="orange")
plot.plot(("index", "y5"), line_width=2, name="x**x", color="red")
# Set the value axis to display on a log scale
plot.value_scale = "log"
# Tweak some of the plot properties
plot.title = "Log Plot"
plot.padding = 50
plot.legend.visible = True
# Attach some tools to the plot
plot.tools.append(PanTool(plot))
zoom = ZoomTool(component=plot, tool_mode="box", always_on=False)
plot.overlays.append(zoom)
return plot
def _create_plot_component():# Create a scalar field to colormap
xbounds = (-2*pi, 2*pi, 600)
ybounds = (-1.5*pi, 1.5*pi, 300)
xs = linspace(*xbounds)
ys = linspace(*ybounds)
x, y = meshgrid(xs,ys)
z = sin(x)*y
# Create a plot data obect and give it this data
pd = ArrayPlotData()
pd.set_data("imagedata", z)
# Create the plot
plot = Plot(pd)
img_plot = plot.img_plot("imagedata",
xbounds = xbounds[:2],
ybounds = ybounds[:2],
colormap=jet)[0]
# Tweak some of the plot properties
plot.title = "My First Image Plot"
plot.padding = 50
# Attach some tools to the plot
plot.tools.append(PanTool(plot))
zoom = ZoomTool(component=plot, tool_mode="box", always_on=False)
plot.overlays.append(zoom)
imgtool = ImageInspectorTool(img_plot)
img_plot.tools.append(imgtool)
overlay = ImageInspectorOverlay(component=img_plot, image_inspector=imgtool,
bgcolor="white", border_visible=True)
img_plot.overlays.append(overlay)
return plot
def _create_plot_component():
# Create some data
index, sorted_vals = _create_data(200)
# Create a plot data obect and give it this data
pd = ArrayPlotData(index = index,
min = sorted_vals[0],
bar_min = sorted_vals[1],
average = sorted_vals[2],
bar_max = sorted_vals[3],
max = sorted_vals[4])
# Create the plot
plot = Plot(pd)
plot.candle_plot(("index", "min", "bar_min", "average", "bar_max", "max"),
color = "lightgray",
bar_line_color = "black",
stem_color = "blue",
center_color = "red",
center_width = 2)
# Tweak some of the plot properties
plot.title = "Candlestick Plot"
plot.line_width = 0.5
plot.padding = 50
# Attach some tools to the plot
plot.tools.append(PanTool(plot, constrain_key="shift"))
zoom = ZoomTool(component=plot, tool_mode="box", always_on=False)
plot.overlays.append(zoom)
return plot
def _plot_default(self):
self.data = self._data_default()
plot = Plot(self.data)
for ii, s_name in enumerate(self._samples):
color = COLORS[ii % len(self._samples)]
plot.plot(('bins',s_name), name=s_name,
type='filled_line',
edge_color=color,
face_color=color,
alpha=0.5,
bgcolor='white',
render_style='hold') # render_style determines whether interpolate
plot.index = plot._get_or_create_datasource('bins') #set index name manually so range selection works
plot.index_scale = 'log'
plot.title = 'Fermi Plot'
plot.padding = 50
plot.legend.visible = True
plot.tools.append(PanTool(plot))
plot.active_tool = RangeSelection(plot)
plot.overlays.append(RangeSelectionOverlay(component=plot))
zoom = ZoomTool(component=plot, tool_mode='box', always_on=False)
plot.overlays.append(zoom)
return plot
def _create_plot_component():
# Use n_gon to compute center locations for our polygons
points = n_gon(center=(0,0), r=3, nsides=4)
# Choose some colors for our polygons
colors = {3:0xaabbcc, 4:'orange', 5:'yellow', 6:'lightgreen'}
# Create a PlotData object to store the polygon data
pd = ArrayPlotData()
# Create a Polygon Plot to draw the regular polygons
polyplot = Plot(pd)
# Store path data for each polygon, and plot
nsides = 3
for p in points:
npoints = n_gon(center=p, r=2, nsides=nsides)
nxarray, nyarray = transpose(npoints)
pd.set_data("x" + str(nsides), nxarray)
pd.set_data("y" + str(nsides), nyarray)
plot = polyplot.plot(("x"+str(nsides), "y"+str(nsides)), type="polygon",
face_color=colors[nsides], hittest_type="poly")[0]
plot.tools.append(DataspaceMoveTool(plot, drag_button="left"))
nsides = nsides + 1
# Tweak some of the plot properties
polyplot.padding = 50
polyplot.title = "Polygon Plot"
polyplot.x_axis.mapper.range.set(low=-10, high=10)
polyplot.y_axis.mapper.range.set(low=-10, high=10)
return polyplot
def updateplots(self):
x = np.sort(np.random.random(100))
y = np.random.random(100)
color = np.exp(-(x ** 2 + y ** 2)) # np.random.random(100)
pd = ArrayPlotData()
pd.set_data("index", x)
pd.set_data("value", y)
pd.set_data("color", color)
# Create some line plots of some of the data
plot = Plot(pd)
# Create a scatter plot and get a reference to it (separate from the
# Plot object) because we'll need it for the regression tool below.
scatterplot = plot.plot(
("index", "value", "color"),
type="cmap_scatter",
color_mapper=reverse(Spectral),
marker="square",
fill_alpha=0.9,
marker_size=6,
bgcolor=QtGui.QColor(240, 240, 240),
)[0]
# Tweak some of the plot properties
plot.padding = 50
# Attach some tools to the plot
plot.tools.append(PanTool(plot, drag_button="right"))
plot.overlays.append(ZoomTool(plot))
# Add the regression tool and overlay. These need to be added
# directly to the scatterplot instance (and not the Plot instance).
regression = RegressionLasso(scatterplot, selection_datasource=scatterplot.index)
scatterplot.tools.append(regression)
scatterplot.overlays.append(RegressionOverlay(scatterplot, lasso_selection=regression))
self.chaco_widget1.visualization.plot = plot
self.chaco_widget2.visualization.plot = plot
def _create_plot_component():
pd = ArrayPlotData(x=random(100), y=random(100))
# Create some line plots of some of the data
plot = Plot(pd)
# Create a scatter plot and get a reference to it (separate from the
# Plot object) because we'll need it for the regression tool below.
scatterplot = plot.plot(("x", "y"), color="blue", type="scatter")[0]
# Tweak some of the plot properties
plot.padding = 50
# Attach some tools to the plot
plot.tools.append(PanTool(plot, drag_button="right"))
plot.overlays.append(ZoomTool(plot))
# Add the regression tool and overlay. These need to be added
# directly to the scatterplot instance (and not the Plot instance).
regression = RegressionLasso(scatterplot,
selection_datasource=scatterplot.index)
scatterplot.tools.append(regression)
scatterplot.overlays.append(RegressionOverlay(scatterplot,
lasso_selection=regression))
return plot
def _pot_plot_default(self):
plot = Plot(self.plot_data)
plot.plot(('potentiometer', ))
plot.padding = (0, 0, 0, 0)
plot.value_mapper.range.low_setting = 0
plot.value_mapper.range.high_setting = 1
return plot
def _acc_y_plot_default(self):
plot = Plot(self.plot_data)
plot.plot(('acc_y', ))
plot.padding = (0, 0, 0, 0)
plot.value_mapper.range.low_setting = 0
plot.value_mapper.range.high_setting = 1
return plot
def _create_plot_component():
# Create some x-y data series (with NaNs) to plot
x = linspace(-5.0, 15.0, 500)
x[75:125] = nan
x[200:250] = nan
x[300:330] = nan
pd = ArrayPlotData(index = x)
pd.set_data("value1", jn(0, x))
pd.set_data("value2", jn(1, x))
# Create some line and scatter plots of the data
plot = Plot(pd)
plot.plot(("index", "value1"), name="j_0(x)", color="red", width=2.0)
plot.plot(("index", "value2"), type="scatter", marker_size=1,
name="j_1(x)", color="green")
# Tweak some of the plot properties
plot.title = "Plots with NaNs"
plot.padding = 50
plot.legend.visible = True
# Attach some tools to the plot
plot.tools.append(PanTool(plot))
zoom = ZoomTool(component=plot, tool_mode="box", always_on=False)
plot.overlays.append(zoom)
return plot
def _fuel_cycle_plot_component(x, y, x_name, y_name):
# Create some data
# Create a plot data obect and give it this data
pd = ArrayPlotData()
pd.set_data("index", x)
pd.set_data("value", y)
# Create the plot
plot = Plot(pd)
plot.plot(("index", "value"),
type="line",
marker="circle",
index_sort="ascending",
color="red",
marker_size=3,
bgcolor="white")
# Tweak some of the plot properties
plot.title = "Fuel Cycle Plot"
plot.line_width = 0.5
plot.padding = 100
plot.x_axis.title = x_name
plot.x_axis.title_font = "Roman 16"
plot.x_axis.tick_label_font = "Roman 12"
plot.y_axis.title = y_name
plot.y_axis.title_font = "Roman 16"
plot.y_axis.tick_label_font = "Roman 12"
# Attach some tools to the plot
plot.tools.append(PanTool(plot))
zoom = ZoomTool(component=plot, tool_mode="box", always_on=False)
plot.overlays.append(zoom)
return plot
def _create_plot_component():
# Create some data
numpts = 1000
x = numpy.arange(0, numpts)
y = numpy.random.random(numpts)
marker_size = numpy.random.normal(4.0, 4.0, numpts)
# Create a plot data object and give it this data
pd = ArrayPlotData()
pd.set_data("index", x)
pd.set_data("value", y)
# Create the plot
plot = Plot(pd)
plot.plot(("index", "value"),
type="scatter",
marker="circle",
index_sort="ascending",
color=(1.0, 0.0, 0.74, 0.4),
marker_size=marker_size,
bgcolor="white")
# Tweak some of the plot properties
plot.title = "Scatter Plot"
plot.line_width = 0.5
plot.padding = 50
# Attach some tools to the plot
plot.tools.append(PanTool(plot, constrain_key="shift"))
zoom = ZoomTool(component=plot, tool_mode="box", always_on=False)
plot.overlays.append(zoom)
return plot
def _create_plot_component(): # Create a scalar field to colormap
xbounds = (-2 * pi, 2 * pi, 600)
ybounds = (-1.5 * pi, 1.5 * pi, 300)
xs = linspace(*xbounds)
ys = linspace(*ybounds)
x, y = meshgrid(xs, ys)
z = sin(x) * y
# Create a plot data obect and give it this data
pd = ArrayPlotData()
pd.set_data("imagedata", z)
# Create the plot
plot = Plot(pd)
img_plot = plot.img_plot("imagedata", xbounds=xbounds[:2], ybounds=ybounds[:2], colormap=jet)[0]
# Tweak some of the plot properties
plot.title = "Image Plot with Lasso"
plot.padding = 50
lasso_selection = LassoSelection(component=img_plot)
lasso_selection.on_trait_change(lasso_updated, "disjoint_selections")
lasso_overlay = LassoOverlay(lasso_selection=lasso_selection, component=img_plot)
img_plot.tools.append(lasso_selection)
img_plot.overlays.append(lasso_overlay)
return plot
def _create_plot_component():
# Create some data
numpts = 1000
x = numpy.arange(0, numpts)
y = numpy.random.random(numpts)
marker_size = numpy.random.normal(4.0, 4.0, numpts)
color = numpy.random.random(numpts)
# Create a plot data object and give it this data
pd = ArrayPlotData()
pd.set_data("index", x)
pd.set_data("value", y)
# Because this is a non-standard renderer, we can't call plot.plot, which
# sets up the array data sources, mappers and default index/value ranges.
# So, its gotta be done manually for now.
index_ds = ArrayDataSource(x)
value_ds = ArrayDataSource(y)
color_ds = ArrayDataSource(color)
# Create the plot
plot = Plot(pd)
plot.index_range.add(index_ds)
plot.value_range.add(value_ds)
# Create the index and value mappers using the plot data ranges
imapper = LinearMapper(range=plot.index_range)
vmapper = LinearMapper(range=plot.value_range)
# Create the scatter renderer
scatter = ColormappedScatterPlot(
index=index_ds,
value=value_ds,
color_data=color_ds,
color_mapper=jet(range=DataRange1D(
low=0.0, high=1.0)),
fill_alpha=0.4,
index_mapper=imapper,
value_mapper=vmapper,
marker='circle',
marker_size=marker_size)
# Append the renderer to the list of the plot's plots
plot.add(scatter)
plot.plots['var_size_scatter'] = [scatter]
# Tweak some of the plot properties
plot.title = "Variable Size and Color Scatter Plot"
plot.line_width = 0.5
plot.padding = 50
# Attach some tools to the plot
plot.tools.append(PanTool(plot, constrain_key="shift"))
zoom = ZoomTool(component=plot, tool_mode="box", always_on=False)
plot.overlays.append(zoom)
return plot
def _create_plot_component(self):
""" Creates the plot component of the to be used in the FeatureScatter
instance.
"""
x = np.zeros(len(self.data))
y = np.zeros(len(self.data))
c = np.zeros(len(self.data))
for i, (coord, count) in enumerate(self.data.items()):
x[i], y[i] = coord
c[i] = count
c = np.log2(c)
pd = ArrayPlotData()
pd.set_data("x", x)
pd.set_data("y", y)
pd.set_data("color", c)
cm = Map(DataRange1D(low=-c.max() / 2, high=c.max()))
plot = Plot(pd)
plot.plot(("x", "y", "color"),
type="cmap_scatter",
name="my_plot",
marker="dot",
index_sort="ascending",
color_mapper=cm,
marker_size=2,
bgcolor=0xF7F7F7,
)
plot.title = "Scatter Plot With Lasso Selection"
plot.line_width = 1
plot.padding = 50
my_plot = plot.plots["my_plot"][0]
my_plot.data = self.data
my_plot.out_file = self.out_file
my_plot.label = self.label
lasso_selection = FeatureLasso(component=my_plot,
selection_datasource=my_plot.index,
drag_button="left")
my_plot.tools.append(lasso_selection)
my_plot.tools.append(BetterZoom(my_plot, zoom_factor=1.2))
my_plot.tools.append(PanTool(my_plot, drag_button="right"))
my_plot.tools.append(ScatterInspector(my_plot))
lasso_overlay = LassoOverlay(lasso_selection=lasso_selection,
component=my_plot,
selection_fill_color=0xEF8A62)
my_plot.overlays.append(lasso_overlay)
my_plot.overlays.append(ScatterInspectorOverlay(my_plot,
hover_marker_size=4))
return plot
def _create_plot_component(obj):
# Setup the spectrum plot
frequencies = linspace(0.0, float(SAMPLING_RATE)/2, num=NUM_SAMPLES/2)
obj.spectrum_data = ArrayPlotData(frequency=frequencies)
empty_amplitude = zeros(NUM_SAMPLES/2)
obj.spectrum_data.set_data('amplitude', empty_amplitude)
obj.spectrum_plot = Plot(obj.spectrum_data)
spec_renderer = obj.spectrum_plot.plot(("frequency", "amplitude"), name="Spectrum",
color="red")[0]
obj.spectrum_plot.padding = 50
obj.spectrum_plot.title = "Spectrum"
spec_range = list(obj.spectrum_plot.plots.values())[0][0].value_mapper.range
spec_range.low = 0.0
spec_range.high = 5.0
obj.spectrum_plot.index_axis.title = 'Frequency (hz)'
obj.spectrum_plot.value_axis.title = 'Amplitude'
# Time Series plot
times = linspace(0.0, float(NUM_SAMPLES)/SAMPLING_RATE, num=NUM_SAMPLES)
obj.time_data = ArrayPlotData(time=times)
empty_amplitude = zeros(NUM_SAMPLES)
obj.time_data.set_data('amplitude', empty_amplitude)
obj.time_plot = Plot(obj.time_data)
obj.time_plot.plot(("time", "amplitude"), name="Time", color="blue")
obj.time_plot.padding = 50
obj.time_plot.title = "Time"
obj.time_plot.index_axis.title = 'Time (seconds)'
obj.time_plot.value_axis.title = 'Amplitude'
time_range = list(obj.time_plot.plots.values())[0][0].value_mapper.range
time_range.low = -0.2
time_range.high = 0.2
# Spectrogram plot
values = [zeros(NUM_SAMPLES/2) for i in range(SPECTROGRAM_LENGTH)]
p = WaterfallRenderer(index = spec_renderer.index, values = values,
index_mapper = LinearMapper(range = obj.spectrum_plot.index_mapper.range),
value_mapper = LinearMapper(range = DataRange1D(low=0, high=SPECTROGRAM_LENGTH)),
y2_mapper = LinearMapper(low_pos=0, high_pos=8,
range=DataRange1D(low=0, high=15)),
)
spectrogram_plot = p
obj.spectrogram_plot = p
dummy = Plot()
dummy.padding = 50
dummy.index_axis.mapper.range = p.index_mapper.range
dummy.index_axis.title = "Frequency (hz)"
dummy.add(p)
container = HPlotContainer()
container.add(obj.spectrum_plot)
container.add(obj.time_plot)
c2 = VPlotContainer()
c2.add(dummy)
c2.add(container)
return c2
def _create_plot_component():
# Create some data
numpts = 1000
x = sort(random(numpts))
y = random(numpts)
color = exp(-(x**2 + y**2))
# Create a plot data obect and give it this data
pd = ArrayPlotData()
pd.set_data("index", x)
pd.set_data("value", y)
pd.set_data("color", color)
# Create the plot
plot = Plot(pd)
plot.plot(("index", "value", "color"),
type="cmap_scatter",
name="my_plot",
color_mapper=jet,
marker = "square",
fill_alpha = 0.5,
marker_size = 6,
outline_color = "black",
border_visible = True,
bgcolor = "white")
# Tweak some of the plot properties
plot.title = "Colormapped Scatter Plot"
plot.padding = 50
plot.x_grid.visible = False
plot.y_grid.visible = False
plot.x_axis.font = "modern 16"
plot.y_axis.font = "modern 16"
# Right now, some of the tools are a little invasive, and we need the
# actual ColomappedScatterPlot object to give to them
cmap_renderer = plot.plots["my_plot"][0]
# Attach some tools to the plot
plot.tools.append(PanTool(plot, constrain_key="shift"))
zoom = ZoomTool(component=plot, tool_mode="box", always_on=False)
plot.overlays.append(zoom)
selection = ColormappedSelectionOverlay(cmap_renderer, fade_alpha=0.35,
selection_type="mask")
cmap_renderer.overlays.append(selection)
# Create the colorbar, handing in the appropriate range and colormap
colorbar = create_colorbar(plot.color_mapper)
colorbar.plot = cmap_renderer
colorbar.padding_top = plot.padding_top
colorbar.padding_bottom = plot.padding_bottom
# Create a container to position the plot and the colorbar side-by-side
container = HPlotContainer(use_backbuffer = True)
container.add(plot)
container.add(colorbar)
container.bgcolor = "lightgray"
return container
def _create_plot_component():
# Create some data
npts = 100
x = sort(random(npts))
y = random(npts)
# Create a plot data obect and give it this data
pd = ArrayPlotData()
pd.set_data("index", x)
pd.set_data("value", y)
pd.set_data("value2", y * 2)
# Create the plot
plot = Plot(pd)
plot.plot(("index", "value"),
type="scatter",
name="my_plot",
marker="circle",
index_sort="ascending",
color="slategray",
marker_size=6,
bgcolor="white")
plot.plot(("index", "value2"),
type="scatter",
name="my_plot",
marker="circle",
index_sort="ascending",
color="red",
marker_size=6,
bgcolor="white")
# Tweak some of the plot properties
plot.title = "Scatter Plot With Selection"
plot.line_width = 1
plot.padding = 50
# Right now, some of the tools are a little invasive, and we need the
# actual ScatterPlot object to give to them
my_plot = plot.plots["my_plot"][0]
# Attach some tools to the plot
my_plot.tools.append(
ScatterInspector(my_plot,
selection_mode="toggle",
persistent_hover=False))
my_plot.overlays.append(
ScatterInspectorOverlay(my_plot,
hover_color="transparent",
hover_marker_size=10,
hover_outline_color="purple",
hover_line_width=2,
selection_marker_size=8,
selection_color="lawngreen"))
my_plot.tools.append(PanTool(my_plot))
my_plot.overlays.append(ZoomTool(my_plot, drag_button="right"))
return plot
def _create_plot_component():
# Create some data
numpts = 1000
x = numpy.arange(0, numpts)
y = numpy.random.random(numpts)
marker_size = numpy.random.normal(4.0, 4.0, numpts)
color = numpy.random.random(numpts)
# Create a plot data object and give it this data
pd = ArrayPlotData()
pd.set_data("index", x)
pd.set_data("value", y)
# Because this is a non-standard renderer, we can't call plot.plot, which
# sets up the array data sources, mappers and default index/value ranges.
# So, its gotta be done manually for now.
index_ds = ArrayDataSource(x)
value_ds = ArrayDataSource(y)
color_ds = ArrayDataSource(color)
# Create the plot
plot = Plot(pd)
plot.index_range.add(index_ds)
plot.value_range.add(value_ds)
# Create the index and value mappers using the plot data ranges
imapper = LinearMapper(range=plot.index_range)
vmapper = LinearMapper(range=plot.value_range)
# Create the scatter renderer
scatter = ColormappedScatterPlot(
index=index_ds,
value=value_ds,
color_data=color_ds,
color_mapper=jet(range=DataRange1D(low=0.0, high=1.0)),
fill_alpha=0.4,
index_mapper = imapper,
value_mapper = vmapper,
marker='circle',
marker_size=marker_size)
# Append the renderer to the list of the plot's plots
plot.add(scatter)
plot.plots['var_size_scatter'] = [scatter]
# Tweak some of the plot properties
plot.title = "Variable Size and Color Scatter Plot"
plot.line_width = 0.5
plot.padding = 50
# Attach some tools to the plot
plot.tools.append(PanTool(plot, constrain_key="shift"))
zoom = ZoomTool(component=plot, tool_mode="box", always_on=False)
plot.overlays.append(zoom)
return plot
def _create_plot_component():
# Generate some data for the eye diagram.
num_samples = 5000
samples_per_symbol = 24
y = demo_data(num_samples, samples_per_symbol)
# Compute the eye diagram array.
ybounds = (-0.25, 1.25)
grid = grid_count(y,
2 * samples_per_symbol,
offset=16,
size=(480, 480),
bounds=ybounds).T
# Convert the array to floating point, and replace 0 with np.nan.
# These points will be transparent in the image plot.
grid = grid.astype(np.float32)
grid[grid == 0] = np.nan
#---------------------------------------------------------------------
# The rest of the function creates the chaco image plot.
pd = ArrayPlotData()
pd.set_data("eyediagram", grid)
plot = Plot(pd)
img_plot = plot.img_plot("eyediagram",
xbounds=(0, 2),
ybounds=ybounds,
bgcolor=(0, 0, 0),
colormap=cool)[0]
# Tweak some of the plot properties
plot.title = "Eye Diagram"
plot.padding = 50
# Axis grids
vgrid = PlotGrid(component=plot,
mapper=plot.index_mapper,
orientation='vertical',
line_color='gray',
line_style='dot')
hgrid = PlotGrid(component=plot,
mapper=plot.value_mapper,
orientation='horizontal',
line_color='gray',
line_style='dot')
plot.underlays.append(vgrid)
plot.underlays.append(hgrid)
# Add pan and zoom tools.
plot.tools.append(PanTool(plot))
zoom = ZoomTool(component=img_plot, tool_mode="box", always_on=False)
img_plot.overlays.append(zoom)
return plot
def _plot_default(self):
plot = Plot(self.data)
plot.plot( ('t', 'y0'), color=colors[0] )
plot.padding=20
plot.padding_left = 40
plot.tools.append(PanTool(plot))#, constrain=True, constrain_direction="y"))
#TODO: zoomtool works on both axes, should only affect y
plot.tools.append(ZoomTool(plot, tool_mode="range", constrain=True, constrain_direction="y"))
return plot
def _plot_default(self):
plot = Plot(self.plot_data)
plot.x_axis = None
plot.y_axis = None
plot.padding = 0
self.tcm = TransformColorMapper.from_color_map(gray)
self.tcm.unit_func = self.unit_map.function
self.unit_map.on_trait_change(self.map_changed, 'function')
plot.img_plot('image', colormap=self.tcm)
return plot
def _plot_default(self):
plot = Plot(self.plot_data)
plot.x_axis = None
plot.y_axis = None
plot.x_grid = None
plot.y_grid = None
plot.padding = 0
plot.plot('image_histogram')
plot.plot('mapped_histogram', type='filled_line', fill_color='yellow')
return plot
def main():
# Create some x-y data series to plot
x = linspace(-2.0, 10.0, 100)
pd = ArrayPlotData(index=x)
for i in range(5):
pd.set_data("y" + str(i), jn(i, x))
# Create some line plots of some of the data
plot = Plot(
pd,
bgcolor="none",
padding=30,
border_visible=True,
overlay_border=True,
use_backbuffer=False)
plot.legend.visible = True
plot.plot(("index", "y0", "y1", "y2"), name="j_n, n<3", color="auto")
plot.plot(("index", "y3"), name="j_3", color="auto")
plot.tools.append(PanTool(plot))
zoom = ZoomTool(component=plot, tool_mode="box", always_on=False)
plot.overlays.append(zoom)
# Create the mlab test mesh and get references to various parts of the
# VTK pipeline
f = mlab.figure(size=(600, 500))
m = mlab.test_mesh()
scene = mlab.gcf().scene
render_window = scene.render_window
renderer = scene.renderer
rwi = scene.interactor
plot.resizable = ""
plot.bounds = [200, 200]
plot.padding = 25
plot.bgcolor = "lightgray"
plot.outer_position = [30, 30]
plot.tools.append(MoveTool(component=plot, drag_button="right"))
container = OverlayPlotContainer(bgcolor="transparent", fit_window=True)
container.add(plot)
# Create the Enable Window
window = EnableVTKWindow(
rwi,
renderer,
component=container,
#istyle_class = tvtk.InteractorStyleSwitch,
#istyle_class = tvtk.InteractorStyle,
istyle_class=tvtk.InteractorStyleTrackballCamera,
bgcolor="transparent",
event_passthrough=True, )
mlab.show()
return window, render_window
def _create_plot_component():
# Create a GridContainer to hold all of our plots: 2 rows, 3 columns:
container = GridContainer(padding=40, fill_padding=True,
bgcolor="lightgray", use_backbuffer=True,
shape=(2,3), spacing=(20,20))
# Create the initial series of data
x = linspace(-5, 15.0, 100)
pd = ArrayPlotData(index = x)
# Plot some bessel functions and add the plots to our container
for i in range(6):
pd.set_data("y" + str(i), jn(i,x))
plot = Plot(pd)
plot.plot(("index", "y" + str(i)),
color=tuple(COLOR_PALETTE[i]), line_width=2.0,
bgcolor = "white", border_visible=True)
# Tweak some of the plot properties
plot.border_width = 1
plot.padding = 0
plot.padding_top = 30
# Attach some tools to the plot
plot.tools.append(PanTool(plot))
zoom = ZoomTool(plot, tool_mode="box", always_on=False)
plot.overlays.append(zoom)
# Add to the grid container (
container.add(plot)
# Set the upper-left plot to only be resizable vertically, and to have a
# fixed horizontal width. This also constrains the width of the first column.
ul_plot = container.components[0]
ul_plot.set(resizable="v", width=200)
ul_plot.overlays.append(PlotLabel("Not horizontally resizable",
component=ul_plot))
# Set the bottom center plot to have a fixed width and height.
# This also constrains the height of the bottom row and the width of
# the middle column.
cplot = container.components[4]
cplot.set(resizable="", bounds=[400,400])
cplot.overlays.append(PlotLabel("Not resizable", component=cplot))
container.padding_top = 50
container.overlays.append(
PlotLabel('Resize the window - some plots resize, others cannot '
'(see source code)',
component=container,
font = "swiss 16",
overlay_position = "top"))
return container
def _create_plot_component():
# Create some data
npts = 200
x = sort(random(npts))
y = random(npts)
# Create a plot data obect and give it this data
pd = ArrayPlotData()
pd.set_data("index", x)
pd.set_data("value", y)
# Create the plot
plot = Plot(pd)
plot.plot(("index", "value"),
type="scatter",
name="my_plot",
marker="circle",
index_sort="ascending",
color="red",
marker_size=4,
bgcolor="white")
# Tweak some of the plot properties
plot.title = "Scatter Plot With Rectangular Selection"
plot.line_width = 1
plot.padding = 50
# Right now, some of the tools are a little invasive, and we need the
# actual ScatterPlot object to give to them
my_plot = plot.plots["my_plot"][0]
# Attach some tools to the plot
rect_selection = RectangularSelection(
component=my_plot,
selection_datasource=my_plot.index,
drag_button="left",
metadata_name='selections',
)
my_plot.tools.append(rect_selection)
my_plot.tools.append(ScatterInspector(my_plot, selection_mode='toggle'))
my_plot.active_tool = rect_selection
lasso_overlay = LassoOverlay(lasso_selection=rect_selection,
component=my_plot)
my_plot.overlays.append(lasso_overlay)
scatter_overlay = ScatterInspectorOverlay(
component=my_plot,
selection_color='cornflowerblue',
selection_marker_size=int(my_plot.marker_size) + 3,
selection_marker='circle')
my_plot.overlays.append(scatter_overlay)
return plot
def create_plot():
# Create some data
numpts = 200
x = sort(random(numpts))
y = random(numpts)
color = exp(-(x**2 + y**2))
# Create a plot data obect and give it this data
pd = ArrayPlotData()
pd.set_data("index", x)
pd.set_data("value", y)
pd.set_data("color", color)
# Create the plot
plot = Plot(pd)
plot.plot(("index", "value", "color"),
type="cmap_scatter",
name="my_plot",
color_mapper=jet,
marker="square",
fill_alpha=0.5,
marker_size=6,
outline_color="black",
border_visible=True,
bgcolor="white")
# Tweak some of the plot properties
plot.title = "Colormapped Scatter Plot"
plot.padding = 50
plot.x_grid.visible = False
plot.y_grid.visible = False
plot.x_axis.font = "modern 16"
plot.y_axis.font = "modern 16"
# Set colors
#plot.title_color = "white"
#for axis in plot.x_axis, plot.y_axis:
# axis.trait_set(title_color="white", tick_label_color="white")
# Right now, some of the tools are a little invasive, and we need the
# actual ColomappedScatterPlot object to give to them
cmap_renderer = plot.plots["my_plot"][0]
# Attach some tools to the plot
plot.tools.append(PanTool(plot, constrain_key="shift"))
zoom = ZoomTool(component=plot, tool_mode="box", always_on=False)
plot.overlays.append(zoom)
selection = ColormappedSelectionOverlay(cmap_renderer,
fade_alpha=0.35,
selection_type="mask")
cmap_renderer.overlays.append(selection)
plot.tools.append(MoveTool(plot, drag_button="right"))
return plot
def create_plot():
# Create some data
numpts = 200
x = sort(random(numpts))
y = random(numpts)
color = exp(-(x**2 + y**2))
# Create a plot data obect and give it this data
pd = ArrayPlotData()
pd.set_data("index", x)
pd.set_data("value", y)
pd.set_data("color", color)
# Create the plot
plot = Plot(pd)
plot.plot(("index", "value", "color"),
type="cmap_scatter",
name="my_plot",
color_mapper=jet,
marker = "square",
fill_alpha = 0.5,
marker_size = 6,
outline_color = "black",
border_visible = True,
bgcolor = "white")
# Tweak some of the plot properties
plot.title = "Colormapped Scatter Plot"
plot.padding = 50
plot.x_grid.visible = False
plot.y_grid.visible = False
plot.x_axis.font = "modern 16"
plot.y_axis.font = "modern 16"
# Set colors
#plot.title_color = "white"
#for axis in plot.x_axis, plot.y_axis:
# axis.set(title_color="white", tick_label_color="white")
# Right now, some of the tools are a little invasive, and we need the
# actual ColomappedScatterPlot object to give to them
cmap_renderer = plot.plots["my_plot"][0]
# Attach some tools to the plot
plot.tools.append(PanTool(plot, constrain_key="shift"))
zoom = ZoomTool(component=plot, tool_mode="box", always_on=False)
plot.overlays.append(zoom)
selection = ColormappedSelectionOverlay(cmap_renderer, fade_alpha=0.35,
selection_type="mask")
cmap_renderer.overlays.append(selection)
plot.tools.append(MoveTool(plot, drag_button="right"))
return plot
def _create_plot_component():
# Create some data
numpts = 5000
x = sort(np.random.random(numpts))
y = np.random.random(numpts)
# Create a plot data object and give it this data
pd = ArrayPlotData()
pd.set_data("index", x)
pd.set_data("value", y)
# Generate Random marker and outline colors
# The arrays must be RGBA color tuples. Create the appropriate arrays.
col_array=np.zeros(len(x),dtype=('f8,f8,f8,f8'))
outline_col_array=np.zeros(len(x),dtype=('f8,f8,f8,f8'))
colors=[]
for i in range(len(x)):
colors.append(random.choice(enable.colors.color_table.keys()))
for idx, color in enumerate(colors):
col_array[idx] = enable.colors.color_table[color]
colors=[]
for i in range(len(x)):
colors.append(random.choice(enable.colors.color_table.keys()))
for idx, color in enumerate(colors):
outline_col_array[idx] = enable.colors.color_table[color]
# Create the plot
plot = Plot(pd)
plot.plot(("index", "value"),
type="scatter",
marker="circle",
index_sort="ascending",
color=col_array,
marker_size=3,
outline_color = outline_col_array,
bgcolor="white")
# Tweak some of the plot properties
plot.title = "Scatter Plot"
plot.line_width = 0.5
plot.padding = 50
# Attach some tools to the plot
plot.tools.append(PanTool(plot, constrain_key="shift"))
zoom = ZoomTool(component=plot, tool_mode="box", always_on=False)
plot.overlays.append(zoom)
return plot
def _main_plot_default(self):
p = Plot(self.plot_data, default_origin='top left')
p.padding = 0
self.original_plot = p.img_plot('original_image', colormap=bone,
alpha=self.original_alpha,
bgcolor=self.background_color_)[0]
self.canny_plot = p.img_plot('canny_plot_image',
alpha=self.canny_alpha)[0]
p.x_axis = None
p.y_axis = None
self.segments_overlay = SegmentsOverlay(component=self.canny_plot,
image_size=self.image.canny_image.shape)
p.overlays.append(self.segments_overlay)
return p
def _plot_default(self):
plot = Plot(self.plot_data)
plot.x_axis = None
plot.y_axis = None
plot.x_grid = None
plot.y_grid = None
plot.padding = 0
plot.plot(('x', 'image_histogram'), render_style='connectedhold')
plot.plot(('x', 'mapped_histogram'), type='filled_line', fill_color='yellow',
render_style='connectedhold', name='mapped_histogram')
intercept_tool = AttributeDragTool(component=plot,
model=self.unit_map, x_attr='intercept')
plot.tools.append(intercept_tool)
return plot
def _time_plot_default(self):
plot = Plot(self.data)
line_plot = plot.plot(('t', 'y'))[0]
line_plot.active_tool = RangeSelection(line_plot, left_button_selects=True)
line_plot.overlays.append(RangeSelectionOverlay(component=line_plot))
self.selection = line_plot.active_tool
plot.padding = 20
plot.padding_left = 50
self.selection.on_trait_change(self.handle_selection_change, 'selection')
return plot
def intensity_histogram_plot_component(self):
data = ArrayPlotData(x=self.bin_edges, y=self.hist)
plot = Plot(data)
plot.plot(
('x', "y"),
type='bar',
color='auto',
bar_width=1,
)
# without padding plot just doesn't seem to show up?
plot.padding = 0
return plot
def _plot_default(self):
plot = Plot(self.data)
plot.plot(('t', 'y0'), color=colors[0])
plot.padding = 20
plot.padding_left = 40
plot.tools.append(
PanTool(plot)) #, constrain=True, constrain_direction="y"))
#TODO: zoomtool works on both axes, should only affect y
plot.tools.append(
ZoomTool(plot,
tool_mode="range",
constrain=True,
constrain_direction="y"))
return plot
def _plot_default(self):
# Create the plot
plot = Plot(self.dataset)
plot.plot(("index", "value"),
type="scatter",
marker='circle',
color='blue')
# Tweak some of the plot properties
plot.title = "Scatter Plot"
plot.line_width = 0.5
plot.padding = 50
return plot
def _main_plot_default(self):
p = Plot(self.plot_data, default_origin='top left')
p.padding = 0
self.original_plot = p.img_plot('original_image',
colormap=bone,
alpha=self.original_alpha,
bgcolor=self.background_color_)[0]
self.canny_plot = p.img_plot('canny_plot_image',
alpha=self.canny_alpha)[0]
p.x_axis = None
p.y_axis = None
self.segments_overlay = SegmentsOverlay(
component=self.canny_plot, image_size=self.image.canny_image.shape)
p.overlays.append(self.segments_overlay)
return p
def _create_plot_component():
red = util.Color(255, 0, 0, 255)
green = util.Color(0, 255, 0, 255)
blue = util.Color(0, 0, 255, 255)
purple = util.Color(125, 0, 255, 255)
white = util.Color(255, 255, 255, 255)
black = util.Color(0, 0, 0, 255)
clear = util.Color(0, 0, 0, 0)
with Timer("Loeading") as arTimer:
#glyphs = npg.load_csv("../data/circlepoints.csv", 1, 2, 3, 4)
#glyphs = npg.load_hdf("../data/CensusTracts.hdf5", "__data__", "LAT", "LON")
glyphs = npg.load_hdf("../data/tweets-subset.hdf",
"test",
"longitude",
"latitude",
vc="lang_primary")
screen = (800, 600)
ivt = util.zoom_fit(screen, glyphs.bounds())
with Timer("Abstract-Render") as arTimer:
image = core.render(
glyphs, infos.encode(["Arabic", "English", "Turkish", "Russian"]),
npg.PointCountCategories(),
npg.Spread(2) + categories.HDAlpha(
[red, blue, green, purple, black], alphamin=.3, log=True),
screen, ivt)
# image = core.render(glyphs,
# infos.valAt(4,0),
# npg.PointCount(),
# npg.Spread(1) + numeric.BinarySegment(white, black, 1),
# screen,
# ivt)
# Create a plot data object and give it this data
pd = ArrayPlotData()
pd.set_data("imagedata", image)
# Create the plot
plot = Plot(pd)
img_plot = plot.img_plot("imagedata")[0]
# Tweak some of the plot properties
plot.title = "Abstract Rendering"
plot.padding = 50
return plot
def _time_plot_default(self):
plot = Plot(self.data)
line_plot = plot.plot(('t', 'y'))[0]
line_plot.active_tool = RangeSelection(line_plot,
left_button_selects=True)
line_plot.overlays.append(RangeSelectionOverlay(component=line_plot))
self.selection = line_plot.active_tool
plot.padding = 20
plot.padding_left = 50
self.selection.on_trait_change(self.handle_selection_change,
'selection')
return plot
def _plot_default(self):
# Create a plot data obect and give it this data
pd = ArrayPlotData()
pd.set_data("imagedata", self.image)
# Create the plot
plot = Plot(pd)
img_plot = plot.img_plot("imagedata")
# Tweak some of the plot properties
plot.padding = 0
# Attach some tools to the plot
plot.tools.append(ClickTool(plot))
return plot
